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Record W2081141826 · doi:10.1155/2014/267917

Correcting Positional Errors in Shore-Based Theodolite Measurements of Animals at Sea

2014· article· en· W2081141826 on OpenAlexaff
Ophélie Sagnol, Femke Reitsma, Christoph Richter, Laurence H. Field

Bibliographic record

VenueJournal of Marine Biology · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTheodoliteShoreGeodesyPosition (finance)Range (aeronautics)Global Positioning SystemRemote sensingGeologyComputer scienceOceanographyEngineering

Abstract

fetched live from OpenAlex

Determining the position of animals at sea can be particularly difficult and yet, accurate range and position of animals at sea are essential to answer a wide range of biological questions. Shore-based theodolite techniques have been used in a number of studies to examine marine mammal movement patterns and habitat use, offering reliable position measurements. In this study we explored the accuracy of theodolite measurements by comparing positional information of the same objects using two independent techniques: a shore-based theodolite station and an onboard GPS over a range of 25 km from the shore-based station. The technique was developed to study the habitat use of sperm whales ( Physeter macrocephalus ) off Kaikoura, New Zealand. We observed that the position accuracy fell rapidly with an increase in range from the shore-based station. Results showed that the horizontal angle was accurately determined, but this was not the case for the vertical angle. We calibrated the position of objects at sea with a regression-based correction to fit the difference in distance between simultaneously recorded theodolite fixes and GPS positions. This approach revealed the necessity to calibrate theodolite measurements with objects at sea of known position.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.036
GPT teacher head0.274
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2014
Admission routes1
Has abstractyes

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